ReSpinQuant: Efficient Layer-Wise LLM Quantization via Subspace Residual Rotation Approximation
Compared to this week’s papers
Verification pending
Use This Via API or MCP
Use Signal Canvas as the narrative proof surface
Signal Canvas is the citation-first public layer for turning one paper into a structured commercialization narrative. Use it to hand off into REST, MCP, Build Loop, and launch-pack execution without losing source lineage.
Freshness
Signal Canvas proof surface
Canonical route: /signal-canvas/respinquant-efficient-layer-wise-llm-quantization-via-subspace-residual-rotation-approximation
- Observed
- 2026-04-14
- Fresh until
- 2026-04-28
- Coverage
- 50%
- Source count
- 3
- Stale after
- 2026-04-28
Proof data is outside the preferred freshness window.
Proof Quality
One canonical proof ledger now drives the badge, counts, indexing, and commercialization gating.
- Last verified
- 2026-04-14
- References
- 0
- Sources
- 3
- Coverage
- 50%
Commercialization rails stay hidden until proof clears: proof_status, references_count.
Search indexing stays off until proof clears: proof_status, references_count.
Agent Handoff
ReSpinQuant: Efficient Layer-Wise LLM Quantization via Subspace Residual Rotation Approximation
Canonical ID respinquant-efficient-layer-wise-llm-quantization-via-subspace-residual-rotation-approximation | Route /signal-canvas/respinquant-efficient-layer-wise-llm-quantization-via-subspace-residual-rotation-approximation
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/respinquant-efficient-layer-wise-llm-quantization-via-subspace-residual-rotation-approximationMCP example
{
"tool": "search_signal_canvas",
"arguments": {
"mode": "paper",
"paper_ref": "respinquant-efficient-layer-wise-llm-quantization-via-subspace-residual-rotation-approximation",
"query_text": "Summarize ReSpinQuant: Efficient Layer-Wise LLM Quantization via Subspace Residual Rotation Approximation"
}
}source_context
{
"surface": "signal_canvas",
"mode": "paper",
"query": "ReSpinQuant: Efficient Layer-Wise LLM Quantization via Subspace Residual Rotation Approximation",
"normalized_query": "2604.11080",
"route": "/signal-canvas/respinquant-efficient-layer-wise-llm-quantization-via-subspace-residual-rotation-approximation",
"paper_ref": "respinquant-efficient-layer-wise-llm-quantization-via-subspace-residual-rotation-approximation",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Evidence Receipt
Route status: buildingClaims: 0
References: Pending verification
Proof: Verification pending
Freshness state: computing
Source paper: ReSpinQuant: Efficient Layer-Wise LLM Quantization via Subspace Residual Rotation Approximation
PDF: https://arxiv.org/pdf/2604.11080v1
Source count: 3
Coverage: 50%
Last proof check: 2026-04-14T16:49:56.049Z
Paper Conversation
Citation-first answers with explicit evidence receipts, disagreement handling, commercialization framing, and next actions.
ReSpinQuant: Efficient Layer-Wise LLM Quantization via Subspace Residual Rotation Approximation
Canonical Paper Receipt
Last verification: 2026-04-14T16:49:56.049ZFreshness: stale
Proof: unverified
Repo: missing
References: 0
Sources: 3
Coverage: 50%
- - repo_url
- - references
- - proof_status
- - proof verification has not been recorded yet
Preparing verified analysis
Dimensions overall score 7.0
GitHub Code Pulse
No public code linked for this paper yet.
Claim map
No public claim map is available for this paper yet.
Startup potential card
Related Resources
Related resources will appear here when this paper maps cleanly to topic, benchmark, or dataset surfaces.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
Lightweight coding agent in your terminal.
Agentic coding tool for terminal workflows.
AI agent mindset installer and workflow scaffolder.
AI-first code editor built on VS Code.
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $10K - $14K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.